In this work, we use a cross attention model, BERT-large [1]} to obtain these predictions. The input to the model concatenates the premise and hypothesis with a separator symbol and the CLS token's embedding is sent through a linear layer to predict three classes: entailment, contradiction and neutral. We trained this model on the MNLI corpus [2]} reaching a development accuracy of 84%. Note that
the training data for the entailment model does not contain any examples from the review domain. But we found that predictions are rather reasonable and even better when a higher threshold is applied on the probability of the entailment label.
